Objective:To investigate the knowledge, attitudes, practices (KAP), and demands of medical staff in family physician teams regarding the application of artificial intelligence (AI) in contracted services, and to analyze the influencing factors.Methods:A cross-sectional study was conducted from June to July 2023. A total of 602 medical staff members from family physician teams in Shanghai Minhang District were selected as subjects. Data on demographics (age, gender, institution, position, education, work experience, household registration, professional title, marital status, fertility status) and KAP/demand regarding AI application in contracted services were collected using a self-designed questionnaire. Intergroup differences were analyzed. Multiple stepwise linear regression was employed to identify the main factors influencing AI application demand.Results:Among the 602 participants, 484 (80.4%) were aged 30-49 years, 466 (77.40%) were females, 559 (92.9%) held a bachelor′s degree or higher, and 505 (83.9%) had intermediate or senior professional titles. The awareness rate for knowledge, positive attitude rate, and practice implementation rate regarding AI application were 47.2% (284/602), 73.1% (440/602), and 32.1% (193/602), respectively. The mean scores for knowledge, attitude, and practice were 15.72±3.40, 18.34±3.41, and 14.60±3.89, respectively. Significant differences were found among the items within each KAP dimension (knowledge: F=7.688, P<0.001; attitude: F=5.106, P<0.001; practice: F=6.763, P<0.001). Within knowledge, item K3 (awareness of intelligent elderly monitoring devices) scored lowest (3.00±0.79), differing significantly from K1, K2, K4, and K5 (all P<0.05). Within attitude, item A5 (willingness to fully trust AI′s accuracy and convenience in contracted services) scored lowest (3.57±0.75), differing significantly from A3 and A4 (all P<0.05). Within practice, item P3 (increasing reliance on AI in daily contracted services) scored lowest (2.79±0.93), differing significantly from P1 and P2 (all P<0.05). KAP scores differed significantly across demographic subgroups. Knowledge scores differed significantly by age, gender, and marital status (all P<0.05). Attitude scores differed significantly by gender, household registration, and fertility status (all P<0.05). Practice scores differed significantly by gender, position, and marital status (all P<0.05). Regarding demand, the most frequently selected areas were follow-up services (28.74%, 173/602), data management (26.25%, 158/602), and data collection (25.42%, 153/602). Univariate analysis identified age, gender, education, professional title, fertility status, and KAP scores as significant factors influencing AI application demand (all P<0.05). Multiple stepwise linear regression revealed that older age ( t=3.905, P<0.001), female gender ( t=3.548, P<0.001), and higher practice scores ( t=-3.044, P=0.002) were significant predictors of greater AI application demand. Conclusions:Significant variations exist in the KAP levels regarding AI application among family physician team members. Gender, age, and practice behavior significantly influence demand. Tailored strategies for different subgroups, coupled with timely targeted training and practical exercises, are recommended to enhance the effective and widespread adoption of AI technology in family physician contracted services.

Objective:To investigate the knowledge, attitudes, practices (KAP), and demands of medical staff in family physician teams regarding the application of artificial intelligence (AI) in contracted services, and to analyze the influencing factors.Methods:A cross-sectional study was conducted from June to July 2023. A total of 602 medical staff members from family physician teams in Shanghai Minhang District were selected as subjects. Data on demographics (age, gender, institution, position, education, work experience, household registration, professional title, marital status, fertility status) and KAP/demand regarding AI application in contracted services were collected using a self-designed questionnaire. Intergroup differences were analyzed. Multiple stepwise linear regression was employed to identify the main factors influencing AI application demand.Results:Among the 602 participants, 484 (80.4%) were aged 30-49 years, 466 (77.40%) were females, 559 (92.9%) held a bachelor′s degree or higher, and 505 (83.9%) had intermediate or senior professional titles. The awareness rate for knowledge, positive attitude rate, and practice implementation rate regarding AI application were 47.2% (284/602), 73.1% (440/602), and 32.1% (193/602), respectively. The mean scores for knowledge, attitude, and practice were 15.72±3.40, 18.34±3.41, and 14.60±3.89, respectively. Significant differences were found among the items within each KAP dimension (knowledge: F=7.688, P<0.001; attitude: F=5.106, P<0.001; practice: F=6.763, P<0.001). Within knowledge, item K3 (awareness of intelligent elderly monitoring devices) scored lowest (3.00±0.79), differing significantly from K1, K2, K4, and K5 (all P<0.05). Within attitude, item A5 (willingness to fully trust AI′s accuracy and convenience in contracted services) scored lowest (3.57±0.75), differing significantly from A3 and A4 (all P<0.05). Within practice, item P3 (increasing reliance on AI in daily contracted services) scored lowest (2.79±0.93), differing significantly from P1 and P2 (all P<0.05). KAP scores differed significantly across demographic subgroups. Knowledge scores differed significantly by age, gender, and marital status (all P<0.05). Attitude scores differed significantly by gender, household registration, and fertility status (all P<0.05). Practice scores differed significantly by gender, position, and marital status (all P<0.05). Regarding demand, the most frequently selected areas were follow-up services (28.74%, 173/602), data management (26.25%, 158/602), and data collection (25.42%, 153/602). Univariate analysis identified age, gender, education, professional title, fertility status, and KAP scores as significant factors influencing AI application demand (all P<0.05). Multiple stepwise linear regression revealed that older age ( t=3.905, P<0.001), female gender ( t=3.548, P<0.001), and higher practice scores ( t=-3.044, P=0.002) were significant predictors of greater AI application demand. Conclusions:Significant variations exist in the KAP levels regarding AI application among family physician team members. Gender, age, and practice behavior significantly influence demand. Tailored strategies for different subgroups, coupled with timely targeted training and practical exercises, are recommended to enhance the effective and widespread adoption of AI technology in family physician contracted services.